Quantitative Result. Tables REF  and  REF  show the comparison results of different algorithms on image generation, image-to-image translation, and super-resolution.
From these two tables, we can obtain the following observations:
1) In the task of image translation, our method can greatly reduce the computational costs in each task and achieve comparable performance as the original uncompressed model.
For example, GCC obtains a lower FID than the original model under the extremely high compression ratio on CycleGAN. Specifically, GCC reduces the MACs of CycleGAN from 56.8G to 2.40G, with compression by 23.6\(\times \)  while FID still reduces 2.22.
2) GCC reduces the computational costs by 82.39% and achieves a higher mIoU than the original model, which establishes a new state-of-the-art performance for the Pix2Pix model.
3) In image generation task, GCC also shows impressive results on low MACs demands of GAN, i.e., SAGAN.
Although the original SAGAN only requires 23.45M MACs to generate a 64\(\times \) 64 pixel image, GCC successes to reduce the MACs by 34.12%.
4) For SRGAN in the super-resolution task, GCC helps to significantly reduce 123.09G MACs, with a compression ratio of 6.4\(\times \) .
It is worth noting that although GCC reduces such a high amount of computational costs, the lightweight model can achieve higher PSNR and SSIM than the original model on each test dataset.
In addition, we also show the results of
employing network pruning methods [1]}, [2]} to compress the generator and directly perform adversarial training with the original discriminator, abbreviated as "Prune", the experimental results demonstrate the importance and effectiveness of our proposed selective activation discriminator.
In summary, Our proposed GCC contributes to removes a large number of redundant calculations while maintains the comparable performances of the original model.
The results provide a feasible solution for demands on a super-computing or mini-computing GAN model.
